System and method for dynamically selecting networked cameras in a video conference

ABSTRACT

Systems and methods are provided for dynamically selecting one or more networked cameras for providing real-time camera feeds to a video conference. The systems and methods may include identifying one or more networked cameras associated with an area of a conference participant. A server may analyze real-time camera feeds from the identified cameras, and select a video feed having a view of the participant. The server may provide the selected feed to the video conference via a conference bridge, and continue monitoring camera feeds of cameras associated with the participant&#39;s area for another camera feed having a better view of the participant. Networked cameras may include fixed and mobile cameras owned and operated by individuals that are not associated with the participant, but who have registered their cameras with the server for use in video conferences.

This application is a continuation of U.S. patent application Ser. No.14/610,164, filed Jan. 30, 2015, which is incorporated herein byreference in its entirety.

FIELD

The present disclosure relates to video conferencing, and morespecifically to dynamically selecting a networked camera feed for avideo conference.

BACKGROUND

Cameras have become ubiquitous. In recent years, camera and networkingtechnologies have advanced greatly while dropping in cost, making smallrobust, and cheap networked cameras available to consumers in manyforms. Today, cameras with streaming capabilities such as securitycameras, dash cams, mobile device cameras, wearable devices such asGoogle Glass™, etc. can capture real-time video and/or audio content,and stream the video and/or audio content in private or public networks.At any given time, many devices with these capabilities surround us inpublic settings, enabling the possibility of providing video camerastreams with multiple views of an individual.

SUMMARY

The present disclosure arises from the realization that many of theavailable camera streams are underused or unused. For example, manysecurity cameras and vehicle dash cameras continuously record video, butthe owner or operator usually does not check the video stream until anevent occurs. Furthermore, many individuals carry their mobileelectronic devices such as smartphones everywhere they go, yet rarely oronly periodically use the mobile device's camera. Additionally, recentwearable cameras such as Google Glass™ place more cameras on individualsand in the public every day. Indeed, the amount of untapped resourcesgrows continuously, in an already robust infrastructure of stationaryand mobile cameras.

Advances in camera technologies have also driven advances inconferencing technologies. Current video conferencing systems enableconference participants in different time zones, countries, and evenhemispheres to interact in real-time. But video conferencing systemstraditionally require the participants to be in front of a videoconferencing camera, or to hold a camera up to their face during theentire conference.

A method of utilizing unused and underused networked cameras to improvevideo conference call experience is disclosed. Disclosed exampleembodiments provide methods and systems for dynamically selectingnetworked cameras having the ability to capture audio and/or video dataassociated with an area proximate to a conference call participant so asto transmit audio and/or video data of the participant, for providing acamera feed of the participant in a video conference.

Consistent with an embodiment disclosed herein, a computer-implementedmethod for selecting a camera feed for a video conference is provided.The method comprises determining a first area of a participant in thevideo conference, receiving registration information for a plurality ofvideo cameras associated with a plurality of subscribers, identifying,from the plurality of cameras, a first set of cameras associated withthe first area; analyzing a first plurality of real-time camera feedsfrom the first set of cameras, and selecting, based on the analysis, afirst camera feed of the first plurality of real-time camera feeds, theselected first camera feed containing a view of the participant.

Consistent with another disclosed embodiment, a system for selecting acamera feed for a video conference is provided. The system comprises oneor more memories having stored thereon computer-executable instructions,and one or more processors configured to execute the storedinstructions. When the stored instructions are executed, the one or moreprocessors can determine a first area of a participant in the videoconference, receiving registration information for a plurality of videocameras associated with a plurality of subscribers, identify, from theplurality of cameras, a first set of cameras associated with the firstarea, analyze a first plurality of real-time camera feeds from the firstset of cameras, and select, based on the analysis, a first camera feedof the first plurality of real-time camera feeds, the selected firstcamera feed containing a view of the participant.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media can store program instructions, whichare executed by at least one processor device and perform any of themethods described herein.

The foregoing general description and the following detailed descriptionare explanatory only and are not restrictive of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate several embodiments and, togetherwith the description, serve to explain the disclosed principles. In thedrawings:

FIG. 1 is an illustration of an example of a system and method ofdynamically selecting networked cameras in a video conference,consistent with disclosed embodiments.

FIG. 2 is a diagram of a system for dynamically selecting cameras in avideo conference, according to the disclosed embodiments.

FIG. 3 is a diagram of an example of a server for dynamically selectingnetworked cameras, consistent with the disclosed embodiments.

FIG. 4 is a component diagram of an example of a camera device,according to the disclosed embodiments.

FIG. 5 is a flowchart of an example of a method for dynamicallyselecting cameras in a video conference, consistent with the disclosedembodiments.

FIG. 6 is an illustration of an example use scenario of the disclosedembodiments.

DESCRIPTION OF THE EMBODIMENTS

The disclosed embodiments concern selecting networked cameras for avideo conference. In some embodiments, a server having one or moreprocessors can receive a plurality of video feeds from camerasassociated with an area of a conference call participant. The camerasinclude devices owned and operated by the participant, as well asnetworked cameras owned and operated by other entities, such as securitycameras in a facility where the participant is located, mobile devicesof other individuals (including complete strangers) walking nearby theparticipant, or outdoor surveillance cameras with a clear view of theparticipant, regardless of the distance. The server can maintain adatabase of the networked cameras, which are pre-registered by thecamera owners and operators.

In some embodiments, the networked camera owners and operators and theparticipant are not aware of one another. That is, there could be nopreexisting relationship or affinity between the networked camera ownersand operators and the participant, and thus the owners and operators maynot be aware of their cameras are streaming video of the participant.The owners and operators may only know that their camera(s) areregistered for use by the one or more processors. In some embodiments,owners and operators can receive compensation for registering theirnetworked camera(s), and granting the server access to their subscribedcamera(s).

As an advantage of the disclosed embodiments, an individual canparticipate in a video conference without having to hold a camerathroughout the conference, or remain stationary in front of a videoconference camera. Instead, the individual can move freely betweendifferent areas during the video conference, and the server can providevideo feeds from networked cameras having suitable real-time video ofthe participant to one or more servers, devices, or conference bridgesassociated with the video conference. Thus, without even needing acamera of his own, the participant can participate in a video conferencerelying upon networked cameras registered by subscribers. As theparticipant moves about an area, the server dynamically selects camerasassociated with an area of the participant, that potentially provide aview of the participant, based on a continuous comparison of real-timecamera feeds from the cameras, to provide the video conference with acamera feed having a view of the participant. In some embodiments, theserver monitors an area in the vicinity of a location of theparticipant, and determines when to analyze feeds from additionalcameras based on changes in the monitored area. In some embodiments, ifthe participant enters an area where there are no networked camerasavailable, the server can alert the participant of the lack in coverage,and allow or activate a camera owned by the participant to continue thevideo feed, such as a mobile device camera, a dash cam, a home securitycamera, etc. In some embodiments, the server may also have a map ofnetworked cameras, and select one or more video feeds from networkedcameras based on the participant's current monitored movement pattern(e.g., based on GPS coordinates), based on the participant's historicmovement patterns, or based on a calendar schedule indicating theparticipant's planned areas.

Other features and advantages of the present embodiments are discussedin further detail below with respect to the figures.

FIG. 1 is an illustration of an example of a system and method ofdynamically selecting networked cameras in a video conference. In thepresently described example, a participant (“P”) 110 is engaged in avideo conference session with one or more other participants 142 a, 142b at a remote location 140. In this example, during the video conferenceparticipant 110 is also attending an event such as a convention. Uponinitiating a video conference, a server 220 determines a first area 100of participant 110 associated with a determined location of participant110 and the proximate vicinity around the determined location. At thefirst area 100, one or more bystanders 112 such as convention attendeemay be present having mobile or wearable camera devices.

As shown in FIG. 1, bystander 112 is wearing a wearable camera 210 a inthe form of video camera glasses such as the Google Glass™ device.Server 220 identifies wearable camera 210 a as a networked cameraassociated with participant's 110 area, and begins analyzing thereal-time camera feed from wearable camera 210 a. If participant 110 iswithin wearable camera 210 a's field of view (denoted by dashed lines),and if the camera feed includes a clear view of participant 110's face,server 220 provides the camera feed to remote location 140 for use inthe video conference with other participants 142 a and 142 b.

In this example, bystander 112 and participant 110 may not know eachother, and may not even interact with one another while wearable camera210 a streams a real-time camera feed to the server 220 for analysis andfor providing to devices associated with the video conference. Bystander112 may only be aware of his wearable camera 210 a being registered withserver 220 (or a service provider associated with server 220), and thatthe camera feed may be analyzed and provided to other individuals. Insome embodiments, bystander 112 can receive compensation for registeringwearable camera 210 a with server 220 and for using wearable camera 210a for the video conference, such as monetary compensation or an accountcredit from a service provider associated with server 220. In someembodiments, bystander 112 can receive compensation up-front, uponregistering his camera device with server 220, in return for grantingserver 220 access to the video and/or audio feed from wearable camera210 a. In other embodiments, bystander 112 may receive compensationbased on an amount or length of camera feed that server 220 receives anduses from wearable camera 210 a during a given period of time.

During the video conference, participant 110 can move around the area,and eventually exit wearable camera 210 a's field of view. For example,participant 110 can walk to another area of the convention at a secondarea 120, where bystander 112 is not present. Server 220 determines thatwearable camera 210 a's camera feed no longer includes an optimal viewof participant 110, and server 220 searches for a new optimal camerafeed.

As discussed herein, a camera feed having an “optimal view” is a camerafeed that includes, for example, a view of participant 110 that is of agreater size and/or resolution than other camera feeds analyzed within aparticular period of time. For example, the term “optimal view” refersto a video or image that includes a highest objectively evaluateddigital representation of participant 110 based on, for example, astatistical ranking or scoring of received camera feeds, the optimalview having the highest ranking or score compared to other camera feedsat the time of evaluation. In some embodiments, server 220 performs arecognition analysis to evaluate camera feeds against a pre-stored imageof participant 110's face, and optionally also body, discussed infurther detail below.

After identifying networked cameras in second area 120, server 220analyzes a camera feed from a security camera such as fixed camera 210b. As shown in FIG. 1, participant 110 is within the field of view offixed camera 210 b, and therefore server 220 provides the camera feedfrom fixed camera 210 b to remote location 140 for use in the videoconference. Server 220 dynamically and seamlessly alternates camerafeeds from wearable camera device 210 a to fixed camera 210 b, onceserver 220 determines that a new optimal feed is required based onparticipant 110's new area, or when server 220 determines that anothernetworked camera feed in the current area provides a better view ofparticipant 110.

Participant 110 can continue walking throughout the convention, andeventually exits the building into a parking lot. As participant 110walks to his car, server 220 searches for networked cameras in a thirdarea 130 associated with a third determined location. In someembodiments, server 220 can predict participant 110's next area based onhistorical data and/or heuristics. For example, if server 220 determinesthat participant 110 is walking toward a familiar networked camera suchas participant 110's vehicle camera in his car, server 220 may assumethat participant 110 will soon appear in vehicle camera 210 c's field ofview. Server 220 can begin analyzing vehicle camera 210 c's camera feed,and provide the camera feed from vehicle camera 210 c to the videoconference at a time just prior to participant 110's expected arrivalwithin vehicle camera 210 c's field of view. In some embodiments, server220 provides the camera feed to the video conference by forwarding thecamera feed to conference bridge 252, which distributes the camera feedto one or more video conference devices 254 a-d (shown in FIG. 2). Inother embodiments, server 220 distributes the camera feed directly tovideo conference devices 254 a-d. Server 220 analyzes one or morecharacteristics of participant 110's area such as direction vector,velocity, acceleration, and historical motion patterns to estimate atime and area where participant 110 will appear next, in order topredictively select the next camera feed for the video conference. Insome embodiments, server 220 awaits confirmation that a predicted camerafeed includes an optimal view of participant 110 before providing thecamera feed to devices associated with the video conference.

In some embodiments, server 220 tracks participant 110 in the one ormore camera feeds received for participant 110's area. In suchembodiments, server 220 can employ one or more methods for trackingindividuals in video feeds such as, for example, facial recognition,object motion tracking, and differentiating between different personsand objects in the video feeds. By tracking participant 110 throughoutthe video feed, server 220 can maintain a sufficient and focused view ofparticipant 110 even in crowds of other bystanders. Additionally,tracking participant 110 can increase server 220's accuracy forpredictively selecting camera feeds for the video conference.

In some embodiments, server 220 analyzes received real-time camerafeeds, and selects a camera feed for the video conference. A camera feedcan be selected based on a comparison of objective and statisticalanalysis results for each of the received camera feeds. For example,server 220 can compare landmarks and features of faces recognized in acamera feed, to a database of facial images of individuals registeredwith server 220, including participant 110. Based on the comparisons,server 220 can perform a dynamic probabilistic analysis to determinewhether participant 110's face appears in at least one of the receivedcamera feeds. Using a dynamic probabilistic analysis, server 220 canscore and/or rank the camera feeds, to select a camera feed with ahighest rank or score among the received camera feeds.

In some embodiments, participant 110 provides an image of themselves viatheir participant device 254 to server 220 at a time prior to the videoconference or at the beginning of the video conference. Server 220 canidentify features such as landmarks on participant 110's face and/orbody, for use in subsequent analysis of received camera feeds. Landmarkscan include, for example, facial features, facial dimensions, bodyshape, height, size, distinctive clothing or accessories, hairstyleshape, hair color, and any other quantifiable characteristics that canassist server 220 in later identifying participant 110 in a real-timeanalysis of received camera feeds.

In some embodiments, server 220 employs one or more facial recognitionalgorithms to quantify facial features of participant 110 such as, forexample, a Principal Component Analysis using eigenfaces, a LinearDiscriminate Analysis, an Elastic Bunch Graph Matching using theFisherface algorithm, a Hidden Markov model, a Multi-linear SubspaceLearning using tensor representation, and/or a neuronal motivateddynamic link matching. Server 220 can utilize one or more knowntechniques and algorithms for analyzing camera feeds, includinggeometric algorithms analyzing distinguishing features on participant110's face and/or body, and search for similar features in the camerafeeds. For example, facial recognition algorithms can extract landmarkor features, from a reference image of participant 110's face. In someembodiments, a facial recognition algorithm can cause server 220 toanalyze the relative position, size, and/or shape of the eyes, nose,cheekbones, and jaw, forehead, hairline, ears, or chin. Extractedfeatures are then used to search for other image frames in receivedcamera feed videos having matching features. In other embodiments,server 220 can utilize one or more statistical techniques and algorithmsfor analyzing camera feeds, which can convert a reference image ofparticipant 110 and received camera feeds to statistical values, andcompare the statistical values of the reference image and camera feedsto identify matches and eliminate variances. In some embodiments, server220 also analyzes skin textures of participant 110 and individualsrecognized in received camera feeds, to locate participant 110 in one ormore camera feeds with increased accuracy. In such embodiments, server220 can quantify unique patterns, lines, spots, or tattoos onparticipant 110, and perform a dynamic probabilistic analysis onreceived camera feeds to find matches.

In some embodiments, server 220 can recognize and identify multipleparticipants of the video conference in the same camera feed, includingparticipant 110 and one or more additional participants located nearparticipant 110. In such embodiments, server 220 can take one or moreactions according to predetermined rules, settings, or preferences setby participant 110, other participants in the video conference, or anadministrator associated with server 220. In some embodiments, server220 can select one or more camera feeds for the video conference toprovide views of all participants. In some embodiments, server 220 canprioritize the recognized participants, and provide one or more views tothe video conference depending on a determined priority for each of theparticipants. For example, server 220 can rank the video conferenceparticipants based on an amount that each participant pays to have theirimage in the video conference. In this example, server 220 can assign ahighest priority to the highest paying participant, and provide a viewof the highest priority participant among the recognized participants.As another example, server 220 can rank recognized participants based onan analysis of the quality of a view of each participant. In thisexample, a participant who appears in multiple simultaneous camera feedsand fills at least a predetermined amount of the image frame can beranked higher than another participant who only appears in a singlecamera feed and/or appears small in the image frame. In this example,server 220 can provide a view of a recognized participant that is morelikely to appear continuously during the video conference due to theavailability of multiple camera feeds having at least one optimal viewof the participant.

In some embodiments, server 220 scans audio signals in received camerafeeds and searches for participant 110's voice in the received camerafeeds. Audio scanning can provide multiple advantages, includingnarrowing the pool of potential camera feeds to those with a relativelystrong voice match, implying that participant 110 may appear in camerafeeds in which participant 110's voice is louder and/or clearer. Forexample, server 220 can search for a voice match to participant 110'svoice, and preliminarily select one or more camera feeds with an audiosignal strength of participant's voice that is above a predeterminedthreshold. Server 220 can compare received camera feed audio signals toone or more previously recorded voice samples of participant 110, tofacilitate the analysis.

In some embodiments, server 220 dynamically changes the audio source ofparticipant 110's voice in the video conference, based on analysis ofparticipant 110's voice from their participant device 254, compared toaudio signals in received camera feeds. If one or more camera feedscontain participant 110's voice at a higher quality than the voicereceived from participant device 254 during the video conference, server220 may dynamically change audio feeds, to provide audio data from acamera feed to the devices associated with the video conference. Forexample, participant 110's participant device 254 may malfunction, losepower, or disconnect from a network during the video conference,resulting in high static levels, interference, or lost audio data ofparticipant 110's voice. Server 220 can dynamically switch audio feedsto provide audio data from one or more camera feeds, to maintainconsistent voice quality during the video conference. In someembodiments, server 220 provides audio data extracted from a camera feedthat is different from the camera feed providing video to the videoconference. Prior to providing the audio data to the video conference,in some embodiments server 220 can process the audio data to filterbackground noise and remove other voices of bystanders.

FIG. 2 is a diagram of a system for dynamically selecting cameras in avideo conference, consistent with disclosed embodiments. As shown inFIG. 2, system 200 includes one or more camera devices 210, server 220,one or more database 230, network 240, local network 242, and conferencesystem 250. The components and arrangements shown in FIG. 2 are notintended to limit the disclosed example embodiment, as the systemcomponents used to implement the disclosed processes and features canvary.

Camera devices 210 can include one or more standalone cameras or deviceshaving embedded cameras such as, for example, wearable camera 210 a,fixed camera 210 b, vehicle camera 210 c, and mobile camera 210 d.Wearable camera 210 a can include a video camera embedded in a deviceconfigured to be attached to an individual, such as a pair of glassesincluding Google Glass™, a hat, a wristwatch, a smart watch, a necklace,a shirt button, an armband, or any other device wearable on a person'sbody. Fixed camera 210 b can include a wired or wireless camerainstalled in a fixed location, such as a surveillance camera, a securitycamera, CCTV (closed-circuit television) is a TV system having one ormore cameras, or a mounted web camera. Vehicle camera 210 c can includea dash camera mounted on the dashboard of a car, or a camera attached toor embedded within a vehicle such as, for example, a boat, a plane, acar, a truck, a helicopter, a remote-controlled autonomous orsemi-autonomous vehicle, an unmanned aerial vehicle (sometimes referredto as a drone), a flying aircraft, a blimp, or a satellite. Mobilecamera 210 d can include a camera attached to or embedded within apersonal electronic device such as a cellular phone, smartphone,personal digital assistant, laptop computer, tablet computer, musicplayer, or any other personal electronic device with sufficientprocessing and networking capabilities.

Server 220 (further described in connection with FIG. 3) can be acomputer-based system including computer system components, desktopcomputers, workstations, tablets, hand held computing devices, memorydevices, and/or internal network(s) connecting the components. Server220 is specially configured to perform steps and functions of thedisclosed embodiments, and in some embodiments server 220 includesspecialized hardware for performing steps of the disclosed methods, suchas, for example, a camera arbitration module (item 360 in FIG. 3).

Database 230 can include one or more physical or virtual storages incommunication with server 220. In some embodiments, database 230communicates with server 220 via a direct wired and/or wireless link. Inother embodiments, database 230 communicates with server 220 via network240 (data path not shown in figures). Database 230 is speciallyconfigured with specialized hardware and/or software configured toperform steps and functions of the disclosed embodiments.

Network 240 comprises any type of computer networking arrangement usedto exchange data. For example, network 240 can be the Internet, aprivate data network, virtual private network using a public network, asatellite link, and/or other suitable connection(s) that enables system200 to send and receive information between the components of system200. Network 240 can also include a public switched telephone network(“PSTN”) and/or a wireless network such as a cellular network, Wi-Finetwork, or other known wireless network capable of bidirectional datatransmission.

Local network 242 can comprise a small-scale wired or wireless networkin the vicinity of one or more camera devices 210 such as a short rangewireless network including Bluetooth™ or Wi-Fi, or a Local Area Network(LAN) or Wireless Local Area Network (WLAN). In some embodiments, one ormore camera devices 210 can communicate with network 240 and/or server220 via local network 242. In other embodiments, camera devices 210 cancommunicate with server 220 via a direct connection to network 240. Insome embodiments, one or more camera devices 210 can communicatedirectly with server 220 via a direct wired or wireless link. It isappreciated that different camera devices 210 can communicate withserver 220 via one or more of the above-described communication schemesdepending on the capabilities of the camera device, the configuration ofthe camera device, and the availability of network 240 and/or localnetwork 242 in the vicinity of the respective camera device 210.

Conference system 250 can comprise at least one conference bridge 252and one or more video conference devices 254 a-d. As shown in FIG. 2,conference system 250 includes multiple types of video conferencedevices. As a first example, video conference devices 254 a-c comprisesa video screen such as a television, computer monitor, or laptopcomputer screen, and a camera such as a web cam. As a second example,video conference device 254 d comprises a mobile device such as asmartphone having display and video capture capabilities. In someembodiments, conference system 250 also includes devices without displayor video capture capabilities, such as a cellular phones or a telephone(not shown). In some embodiments, video conference devices 254 a-d areoperated by one or more conference participants such as participant 110and other participants 142. In the example illustrated in FIG. 1,participant 110 operates a smartphone such as video conference device254 d, and other participants 142 a and 142 b operate one or more videoconference devices 254 a-c.

Conference bridge 252 comprises a device or group of devices configuredto connect video conference devices 254 a-d in a conference call.Conference bridge 252 can comprise one or more processors for performingfunctions related to the disclosed methods, such as receiving camerafeed data from server 220, and providing camera feeds to devicesassociated with the video conference, such as video conference devices254 a-d. In some embodiments, conference bridge 252 comprises a softwaremodule executed by one or more processors of server 220.

FIG. 3 shows a diagram of an example of server 220, consistent with thedisclosed embodiments. As shown, server 220 includes one or moreprocessors 310, input/output (“I/O”) devices 350, and one or morememories 320 storing programs 330 including, for example, server app(s)332, operating system 334, and storing data 340, a database 230, and acamera arbitration module 360. In some embodiments, server 220 alsoincludes one or more hardware and/or software modules for performingspecific steps and functions associated with the disclosed methodsincluding, for example, camera arbitration module 360 that receives atleast one camera feed via a wired or wireless link, and provides the atleast one camera feed to processor 310. Server 220 can be a singleserver or can be configured as a distributed computer system includingmultiple servers or computers that interoperate to perform one or moreof the processes and functionalities associated with the disclosedembodiments.

Processor 310 can be one or more processing devices configured toperform functions of the disclosed methods. Processor 310 can constitutea single core or multiple core processors executing parallel processessimultaneously. For example, processor 310 can be a single coreprocessor configured with virtual processing technologies. In certainembodiments, processor 310 uses logical processors to simultaneouslyexecute and control multiple processes. Processor 310 can implementvirtual machine technologies, or other known technologies to provide theability to execute, control, run, manipulate, store, etc. multiplesoftware processes, applications, programs, etc. In another embodiment,processor 310 includes a multiple-core processor arrangement (e.g.,dual, quad core, etc.) configured to provide parallel processingfunctionalities to allow server 220 to execute multiple processessimultaneously. As discussed in further detail below, processor 310 isspecially configured with one or more applications and/or algorithms forperforming method steps and functions of the disclosed embodiments. Forexample, processor 310 (and server 220) can be configured with hardwareand/or software components that enable processor 310 to receive multiplesimultaneous real-time camera feeds, analyze the camera feeds inreal-time, select a camera feed with an optimal view of a participant,and provide the selected camera feed to one or more devices associatedwith a video conference. As another example, processor 310 (and server220) can be configured with hardware and/or software components thatenable processor 310 to determine an area of at least one participant,identify networked cameras associated with the determined area, anddetermine changes in the participant's area and/or networked camerasassociated with the area. It is appreciated that other types ofprocessor arrangements could be implemented that provide for thecapabilities disclosed herein.

Memory 320 can be a volatile or non-volatile, magnetic, semiconductor,tape, optical, removable, non-removable, or other type of storage deviceor tangible and/or non-transitory computer-readable medium that storesone or more program(s) 330 such as server apps 332 and operating system334, and data 340. Common forms of non-transitory storage media include,for example, a flash drive, a flexible disk, hard disk, solid statedrive, magnetic tape, or any other magnetic data storage medium, aCD-ROM, any other optical data storage medium, any physical medium withpatterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM or any otherflash memory, NVRAM, a cache, a register, any other memory chip orcartridge, and networked versions of the same.

Server 220 includes one or more storage devices configured to storeinformation used by processor 310 (or other components) to performcertain functions related to the disclosed embodiments. For example,server 220 can include memory 320 that includes instructions to enableprocessor 310 to execute one or more applications, such as serverapplications 332, operating system 334, and any other type ofapplication or software known to be available on computer systems.Alternatively or additionally, the instructions, application programs,etc. can be stored in an internal database 230 or external storage indirect communication with server 220 (not shown), such as one or moredatabase or memory accessible over network 240.

Database 230 or other external storage can be a volatile ornon-volatile, magnetic, semiconductor, tape, optical, removable,non-removable, or other type of storage device or tangible (i.e.,non-transitory) computer-readable medium. Memory 320 and database 230can include one or more memory devices that store data and instructionsused to perform one or more features of the disclosed embodiments.Memory 320 and database 230 can also include any combination of one ormore databases controlled by memory controller devices (e.g., server(s),etc.) or software, such as document management systems, Microsoft SQLdatabases, SharePoint databases, Oracle™ databases, Sybase™ databases,or other relational databases.

In some embodiments, server 220 is communicatively connected to one ormore remote memory devices (e.g., physical remote databases or remotedatabases on a cloud storage system (not shown)) through network 240 ora different network. The remote memory devices can be configured tostore information that server 220 can access and/or manage. By way ofexample, the remote memory devices could be document management systems,Microsoft SQL database, SharePoint databases, Oracle™ databases, Sybase™databases, or other relational databases. Systems and methods consistentwith disclosed embodiments, however, are not limited to separatedatabases or even to the use of a database.

Programs 330 include one or more software modules causing processor 310to perform one or more functions of the disclosed embodiments. Moreover,processor 310 can execute one or more programs located remotely fromaccount information display system 200. For example, server 220 canaccess one or more remote programs that, when executed, performfunctions related to disclosed embodiments. In some embodiments,programs 330 stored in memory 320 and executed by processor(s) 310 caninclude one or more server app(s) 332 and operating system 334.

Server app(s) 332 can cause processor 310 to perform one or morefunctions of the disclosed methods. For example, server app(s) 332 causeprocessor 310 to determine and monitor one or more areas of one or moreparticipants in a video conference, identify one or more networkedcameras based on the determined area(s), receive information for the oneor more networked cameras, receive real-time camera feeds from theidentified cameras, analyze the received camera feeds using, forexample, facial recognition methods, select a camera feed of thereceived camera feeds, and provide the selected camera feed to the videoconference. Server app(s) 332 can include additional or fewer functionsbased on the configuration of system 200. In some embodiments othercomponents of system 200 are configured to perform one or more functionsof the disclosed methods. For example, video conference devices 254 canbe configured to analyze received camera feeds, and select an optimalcamera feed for display during the video conference.

In some embodiments, program(s) 330 include operating systems 334 thatperform known operating system functions when executed by one or moreprocessors such as processor 310. By way of example, operating systems334 include Microsoft Windows™, Unix™, Linux™, Apple™ operating systems,Personal Digital Assistant (PDA) type operating systems, such asMicrosoft CE™, or other types of operating systems 334. Accordingly,disclosed embodiments can operate and function with computer systemsrunning any type of operating system 334. Server 220 can also includecommunication software that, when executed by a processor, providescommunications with network 240, local network 242, and/or a directconnection to one or more camera device 210.

In some embodiments, data 340 include, for example, registrationinformation for one or more camera devices 210 including, for example,identifying information of the owner or operator of the camera device, ageographic area of the camera device, specifications for the cameradevice such as video resolution, field of view angle, zoom capability,and preferences set by the owner and operator such as dates and times ofcamera availability, dates and times of restrictions, area restrictions,and other restrictions on recording and camera feed transmission.

Server 220 can also include one or more I/O devices 350 having one ormore interfaces for receiving signals or input from devices andproviding signals or output to one or more devices that allow data to bereceived and/or transmitted by server 220. For example, server 220 caninclude interface components for interfacing with one or more inputdevices, such as one or more keyboards, mouse devices, and the like,that enable server 220 to receive input from an operator oradministrator (not shown).

In some embodiments, server 220 includes a camera arbitration module360, comprising hardware and/or software components specificallyconfigured for performing steps and functions of the disclosed methods.For example, camera arbitration module 360 can include one or morephysical or virtual ports for connecting to one or more camera devices210, for receiving camera feeds and for routing a selected camera feedto one or more devices associated with a video conference, such asconference bridge 254. Camera arbitration module 360 can also includeone or more software modules or logic for switching between camerafeeds, encoding and/or decoding camera feeds, and processing camerafeeds in accordance with steps of the disclosed methods.

FIG. 4 is a component diagram of an example of a camera device 210.Camera device 210 can be a standalone camera, or can be an electronicdevice having an embedded video camera. As shown, camera device 210 caninclude one or more processor 410, camera 420, memory 430, microphone440, and transceiver 450. Camera device 210 can include additional orfewer components depending on the type of electronic device.

Processor 410 is one or more processing devices configured to performfunctions of the disclosed methods, such as those discussed above withrespect to processor 310. In some embodiments, processor 410 can beconfigured to execute computer instructions to receive instructions fromserver 220, capture video data, and transmit real-time captured videodata to server 220.

Camera 420 includes one or more sensors for converting optical images todigital still image and/or video data. The one or more image sensors caninclude known sensors such as semiconductor charge-coupled devices(CCD), complementary metal-oxide-semiconductor (CMOS) devices, and otherdevices capable of providing image data to processor 410.

Memory 430 can be a volatile or non-volatile, magnetic, semiconductor,tape, optical, removable, non-removable, or other type of storage deviceor tangible (i.e., non-transitory) computer-readable medium that storescomputer executable code such as firmware that causes processor 410 toperform one or more functions associated with image capture, dataprocessing, data storage, transmitting data via transceiver 450, andreceiving data via transceiver 450. In some embodiments, memory 430 caninclude one or more buffers for temporarily storing image data receivedfrom camera 420, before transmitting the image data as a camera feed toserver 220.

Microphone 440 can include one or more sensor for converting acousticwaves proximate to camera device 210 to a stream of digital audio data.In some embodiments, camera device 210 transmits a camera feed to server220 including video image and audio data, and in some embodiments cameradevice 210 transmits a camera feed including only video image data.

Transceiver 450 includes a wired or wireless communication modulecapable of sending and receiving data via network 240, local network242, and/or other direct communication links with one or more componentsin system 200. In some embodiments, transceiver 450 can receive datafrom server 220 including instructions for processor 410 to activatecamera 420 to capture video data, and for processor 410 to transmit acamera feed via transceiver 450. In response to the receivedinstructions, transceiver can packetize and transmit a camera feedincluding audio and/or video image data to server 220.

FIG. 5 is a flowchart of a method 500 for dynamically selecting camerasin a video conference. Method 500 describes a process for determining aarea of a conference participant such as participant 110, identifyingnetworked cameras associated with the determined area, selecting acamera for providing a real-time camera feed to the conference, anddynamically changing camera feeds between cameras depending on theparticipant's area. In some embodiments, processor 310 can performprocess 500 while a video conference is in progress between participant110 and other participants 142 a and 142 b. In some embodiments,participant 110 can join the video conference by entering a passcode,credentials, or by facial recognition using a camera device 210. In someembodiments, processor 310 can perform method 500 for more than oneparticipant in the video conference. Although certain steps of method500 are described as being performed by the processor 310 of server 220,one or more processors other than processor 310 can perform one or moresteps of method 500.

Method 500 can begin in step 502, where processor 310 determines a firstarea associated with a location of participant 110. In some embodiments,processor 310 determines an area using locating mechanisms such as GPSdata, assisted GPS (A-GPS), cellular triangulation, or other knownlocating techniques. An area can comprise one or more of latitude andlongitude coordinates, a street address, or an identification of aparticular building or event. Processor 310 can receive raw data from avideo conference device 254 associated with participant 110, anddetermine participant 110's area by analyzing the received raw data. Inother embodiments, participant 110's video conference device 254 candetermine its own area by processing raw data, and transmit thedetermined area to server 220.

In step 504, processor 310 receives camera information from one or morenetworked cameras, such as camera devices 210. In some embodiments,camera devices 210 are registered with server 220 during a registrationprocess (not shown in figures) in which the owners and operators of eachcamera device 210 provide information about the camera device such as,for example, a network address, a location of the camera device 210, atype of device, and the device capabilities. During such a registrationprocess, camera device 210 can transmit one or more signals viatransceiver 450 for self-identifying and/or self-authenticating thecamera device 210 with server 220, and for providing server 220 with anetwork and/or physical address of camera device 210. Owners andoperators can also grant server 220 access to the camera device 210 toperform functions such as powering on, recording video, changing viewingangle, zooming, focusing, and providing a camera feed to server 220.During the registration process, the owners and operators can alsoprovide information for one or more accounts, such as a financialaccount or a subscription service account. In such embodiments, theowners and operators are “subscribers” of a service in which theircamera device 210 resources are leveraged in exchange for a form offinancial compensation. For example, subscribers can receivecompensation proportionate to the amount of time or the bandwidth usagewhile their camera device 210 provided a camera feed to one or morevideo conferences. In some embodiments, subscribers can receivecompensation related to a quality of the camera device 210 location. Forexample, a a camera device 210 located in a busy urban location such asTimes Square in New York City can be associated with a higher rate oramount of compensation, whereas a camera device 210 located in a quietrural area can be associated with a relatively lower rate or amount ofcompensation. In some embodiments, subscribers can receive compensationproportionate to an amount of bandwidth used by the camera device 210 toprovide camera feeds to server 220, whether or not server 220 selectsthe camera feed, to compensate the subscribers for their data usageassociated with requests from server 220. In some embodiments,subscribers can receive compensation based on a total amount of revenuereceived from video conference participants over a predetermined timeperiod, divided among a number of total subscribers. In someembodiments, different types of camera devices 210 can be associatedwith different compensation rates, based on the capabilities andvideo/audio quality of the camera device. Thus, some embodiments yield afinancial gain for subscribers, in addition to providing enhancements invideo conferencing technologies and better use of computing resources.

In some embodiments, database 230 can store camera information for oneor more registered camera devices 210. In some embodiments, memory 320can store camera information as data 340, or server 220 can access aremote storage location for camera information (not shown in figures).

In step 506, processor 310 identifies one or more cameras associatedwith participant 110's determined area. In some embodiments, processor310 can scan the received camera location information to identify one ormore camera devices 210 proximate to participant 110's area.

In some embodiments, processor 310 considers multiple factors whenidentifying camera devices 210 as potential candidates for camera feedanalysis. For example, processor 310 can consider a sensor device 210location and the zoom or resolution capabilities of the sensor device210, to determine whether the camera device 210 may be considered“associated” with the area. A wearable camera 210 a can have limitedzoom and resolution capabilities, requiring the wearable camera 210 a tobe in close physical proximity to participant 110. A larger zoomsurveillance camera, however, provides a sufficient view of participant110 even from a great distance. As another example, an aircraft orsatellite camera feed can contain a sufficient view of participant 110from very far distances. Thus, in step 506 processor 310 can identifycamera devices 210 that are physically proximate to participant 110'sdetermined area, and camera devices 210 which are farther away but havegreater zoom and resolution capabilities.

In step 508, processor 310 determines whether camera devices 210 areunavailable for the determined area. In some embodiments, step 508 is asimple determination of whether zero cameras were identified in step506. For example, if participant 110 is located in a remote, unpopulatedarea, there may be no camera devices 210 proximate to participant 110and no camera devices 210 with a sufficient view of participant 110 froma distance. As another example, camera devices 210 might be unavailablein an area where access to network 240 is limited or insufficient fortransmitting a camera feed.

In some embodiments, step 508 comprises determining whether one or morecamera devices 210 are unavailable due to privacy settings set by anetwork administrator or an owner and operator of camera device 210, oran indication from the network administrator or owner or operator ofcamera device 210 that a camera feed should not be provided to server220 at the time server 220 inquires. For example, an owner and operatorcan include in their camera device 210 information stored as data 340 orin database 230 a restriction on providing camera feeds between thehours of 8:00 PM-6:00 AM, or a restriction on providing camera feedswhen the camera device 210 is located in the owner and operator'sresidence or other designated location.

If at least one identified camera device 210 is available (“No” in step508), then in step 510, processor 310 receives real-time camera feedsfrom the available identified camera devices 210. In some embodiments,processor 310 sends a request to each camera device 210 to begintransmitting a real-time camera feed to server 220. In some embodiments,processor 310 sends instructions to control a camera device 210 to poweron the camera device, begin capturing real-time camera feed data,including video and/or audio data, and begin transmitting a camera feedcomprising the real-time camera feed data.

In step 512, processor 310 analyzes received camera feeds, to identify acamera feed for providing to the video conference. Analysis can include,for example, video quality analysis and/or facial recognition analysis.In some embodiments, processor 310 analyzes each camera feed inreal-time to identify a camera feed having a facial view of participant110. In such embodiments, database 230 and/or memory 320 stores facialrecognition data for participant 110, collected at a time prior to theconference. In some embodiments, participant 110 can provide facialrecognition data through their own camera device upon registering withserver 220, or upon beginning the video conference (steps not shown infigures). Thereafter, processor 310 can compare stored facialrecognition data to the received camera feeds, to identify a camera feedwith the optimal view of participant 110.

In some embodiments, processor 310 performs step 512 continuously duringthe video conference, even as a background operation, while performingother steps of method 500. Continuous camera feed analysis can ensurethat the optimal view of participant 110 is provided to the videoconference at all times.

In step 514, processor 310 can select an optimal camera feed having anoptimal view, based on the analysis. As optimal view can comprise acamera feed with the clearest view of participant 110's face byincluding the most facial features of participant 110, and/or a camerafeed with the largest image of participant 110's face. In someembodiments, the “optimal” camera feed comprises a camera feed with theclearest view of participant 110 relative to the other received camerafeeds, based on a ranking of the camera feeds from the facialrecognition analysis.

In some embodiments, the “optimal” camera feed comprises a camera feedthat meets certain criteria and exceeds certain quality thresholds. Forexample, processor 310 can seek a camera feed in which participant 110'sface fills a predetermined percentage of the image frame, indicative ofa sufficiently close view of participant 110.

In some embodiments, processor 310 can send one or more instructions toidentified camera devices 210 to change an optical focus of the image.For example, processor 310 can instruct the identified camera devices toscan through a focus range while analyzing received camera feeds.Processor 310 can employ one or more known image focus detectionalgorithms, such as contrast focus detection or phase focus detection,to control each camera device 210 and receive the sharpest possibleimages in the received camera feeds.

As shown in FIG. 5, processor 310 continuously loops steps 510, 512, and514, to ensure the highest quality camera feed is provided to the videoconference at all times. Thus, even once a first optimal camera feed isselected in step 514, another camera feed can later be identified asoptimal, and can be selected as a second optimal camera feed to replacethe first optimal camera feed in the video conference. In someembodiments, processor 310 can employ a predetermined time delay beforeselecting a different optimal camera feed, to prevent switching betweencamera feeds too rapidly.

In step 516, processor 310 provides the selected camera feed to thevideo conference. In some embodiments, processor 310 forwards the camerafeed to conference bridge 252 for distribution to video conferencedevices 254. In other embodiments, processor 310 distributes the camerafeed directly to video conference devices 254.

In step 518, processor 310 can determine whether participant 110's areahas changed. In some embodiments, processor 310 determines participant110's current area, compare the current area against the first areadetermined in step 502, and determine that participant 110's locationhas changed when the difference between areas exceeds a predeterminedthreshold. In other embodiments, processor 310 identifies any change inparticipant 110's coordinates or address as an area change.

If participant 110's area has changed (“Yes” in step 518), then method500 returns to step 506, to identify camera devices 210 associated withparticipant 110's new area. Some previously-identified camera devices210 can remain in the group of identified cameras, such as whenparticipant 110 moves only slightly, or when the camera device 210 moveswith participant 110. Thereafter, method 500 continues to step 508 withthe new or modified set of identified camera devices 210.

Returning to step 518, if participant 110's area has not changed morethan a predefined threshold (“No” in step 518), processor 310 continuesproviding the selected camera feed to the video conference, returning tostep 516 via path 520.

Returning to step 508, if no cameras are associated with participant110's location, or none of the identified camera devices are available(“Yes” in step 508), then in step 522 processor 310 determines whetherto utilize a camera feed from participant 110's own camera device 210and/or video conferencing device 254. In some embodiments, participant110 provides one or more preference settings to server 220, such asindicating whether participant 110 prefers to use their own camera feedwhen other camera devices 210 are unavailable, or whether no video feedshould be used when camera devices 210 are unavailable. In someembodiments, processor 310 sends a notification to participant 110'svideo conference device 254, informing participant 110 that cameradevices 210 are unavailable in participant 110's current area, andinquiring whether participant 110's own camera feed should be providedto the video conference (step not shown).

If participant 110's own camera feed should not be used (“No” in step522), then in step 528 processor 310 can discontinue providing anycamera feed of participant 110 to the video conference. In someembodiments, processor 310 can inform participant 110 that the camerafeed is discontinued, and instruct participant 110 to change their areato reinstate the camera feed. Processor 310 can continue monitoringparticipant 110's area for changes (step 518), until participant 110changes area and processor 310 can identify a new set of camera devices210 (“Yes” in step 518).

Returning to step 522, if processor 310 determines that participant110's own camera should be used (“Yes” in step 522), based on personalpreferences or an instruction received from participant 110, thenprocessor 310 can begin receiving a real-time camera feed from one ormore camera devices 210 associated with participant 110, and provide thecamera feed to the video conference (step 524).

Processor 310 can continue monitoring participant 110's area for changes(step 518). If participant 110's area has not changed (“No” in step518), then processor 310 continues providing participant 110's owncamera feed to the video conference, returning to step 524 via path 526.If participant 110's area has changed (“Yes” in step 518), then method500 returns to step 506 to identify cameras associated with the newarea.

In some embodiments, processor 310 continues searching for cameradevices 210 associated with participant 110's area when usingparticipant 110's own camera feed. If processor 310 identifies anavailable camera device 210 associated with participant 110's area,processor 310 automatically provides a camera feed from the cameradevice 210 to the video conference, thereby discontinuing the camerafeed from participant 110's own camera (step not shown). In someembodiments, processor 310 sends one or more inquiries to participant110 via video conference device 254, to determine whether participant110 wishes to continue using their own camera feed, and ignores camerafeeds from other camera devices 210 (steps not shown). Method 500 cancontinue until the video conference ends or until participant 110 and/orother participants 142 a and 142 b instruct server 220 to discontinuethe video portion of the conference, at which point method 500 ends(steps not shown in figure).

FIG. 6 is an illustration of a scenario using the disclosed embodiments.In FIG. 6, a walking path of participant 110 is shown, as participant110 walks throughout interior room 1 610 and interior room 2 620, andinto outdoor area 630. Participant 110 can participate in a videoconference while traversing the walking path, and server 220 can analyzecamera feeds from one or more camera feeds 1-13 from associated cameradevices in FIG. 6. Camera devices associated with each of camera feeds1-13 are represented by a small circle, and dashed lines adjacent toeach camera device circle indicates a field of view of each respectivecamera feed 1-13.

Participant 110 begins standing at position A in interior room 1 610.Processor 310 can determine participant 110's area at position A, andidentify camera devices associated with position A, resulting in firstcamera device set 612. First camera feed set 612 includes camera feeds1, 2, 3, 4, 5, 6, 7, 8, and 9. As shown, the camera devices 210 of firstcamera feed set 612 are located proximate to position A. Server 220 canreceive real-time camera feeds 1, 2, 3, 4, 5, 6, 7, 8, and 9, andanalyze the received camera feeds to select an optimal camera feedhaving the optimal view of participant 110. As shown in FIG. 6, server220 determines whether participant 110 is within the field of view ofone or more camera devices 210, and then determines which of the cameradevices 210 provides an optimal view. For example, participant 110 iswithin camera feed 1's field of view when standing at position A.Although a camera device providing camera feed 2 may be the samedistance or closer to participant 110, camera feed 2 does not containparticipant 110 within its field of view, and thus camera feed 2 doesnot contain an optimal view of participant 110. Furthermore, camera feed4 may not provide an optimal view of participant 110, even thoughparticipant 110 may be within camera feed 4's field of view. Therefore,processor 310 can select a camera feed from camera feed 1 as the optimalcamera feed, and provide the camera feed to the video conference such asby forwarding the camera feed to conference bridge 252.

In some embodiments, processor 310 can determine that participant 110 iswithin a camera device 210's field of view based on a reflection ormirror image of participant 110. For example, if participant 110 islocated behind a camera device 210 or otherwise out of the camera device210's field of view, a mirror or window located within camera device210's field of view can provide camera device 210 with a reflectionand/or reflection of participant 210. In some embodiments, processor 310selects a camera feed having a reflection or mirror image of participant110, when the camera feed is deemed “optimal” in comparison to otherreceived camera feeds.

As participant 110 walks through interior room 1 610 toward position B,processor 310 determines through continuous camera feed analysis thatcamera feed 4 includes a better view of participant 110. Processor 310can identify camera feed 4 as an optimal camera feed, and dynamicallyswitch the camera feed provided to conference bridge 252 to camera feed4, as shown by transition 614.

Participant 110 then proceeds to walk toward position C, and processor310 identifies camera feed 5 as providing an optimal view of participant110. Thus, near position C, server 220 can dynamically switch theprovided camera feed from camera feed 4 to camera feed 5, as shown bytransition 616. Notably, a camera device providing camera feed 6 may belocated closest to participant 110 at position C, but camera feed 6 maynot include any view of participant 110. As discussed herein, selectionof an optimal camera feed may depend on multiple factors such asproximity between participant 110 and the camera device 210, a videoquality of the camera feed, and a facial recognition analysis toidentify the optimal view of participant 110.

Participant 110 continues walking from interior room 1 610 into interiorroom 2 620, and processor 310 determines that participant 110's area haschanged significantly, requiring a new identification of camera devices210 associated with participant 110's new area. Processor 310 canidentify second camera feed set 622, including camera feeds 1, 2, 5, 6,7, 8, 9, 10, and 11 as associated with participant 110's new area, andserver 220 may receive camera feeds 1, 2, 5, 6, 7, 8, 9, 10, and 11.Camera devices associated with camera feeds 3 and 4 are deemed too farfrom participant 110 for inclusion in second camera feed set 622,especially when processor 310 determines that, based on the capabilitiesindicated in the stored camera device information, that camera feeds 3and 4 do not provide a wide-angle or zoom view.

In some embodiments, processor 310 can also identify one or moreintermediate camera feed sets in between 612 and 622, by identifyingcamera devices 210 associated with intermediate participant 110 areas,such as position B and/or position D (extra camera feed sets not shownin figures).

After participant 110 enters interior room 2 620, processor 310 candynamically switch camera feeds provided to conference bridge 252, fromcamera feed 5 to camera feed 8. As shown in FIG. 6, camera feed 8provides the optimal view of participant 110 at position D. Althoughparticipant 110 walks through a field of view of camera feed 7, thecamera feed may not include a sufficient view of participant 110's face,and may only include side and posterior views of participant 110,whereas camera feed 8 may provide a view of participant 110's face.

As participant 110 walks toward position E, camera feed 8 may include aprogressively poorer view of participant 110. Processor 310 can alsodetermine that camera feed 9 does not include a view of participant110's face, but that camera feed 10 provides the optimal view ofparticipant 110. Camera feed 10 may include a zoomed-view of participant110, such as when camera feed 10 is provided by a fixed surveillancecamera. In some embodiments, processor 310 sends instructions to acamera device 210 providing camera feed 10, to zoom-in the camera feedimage, or to rotate the camera device 210. Some camera devices 210 canallow processor 310 to control aspects of the camera device 210, howeversome camera devices such as wearable cameras 210 a may not receive orprocess instructions to rotate the camera or zoom the camera feed image.

Participant 110 exits interior room 2 620, and enters outdoor area 630,prompting processor 310 to identify a new or modified set of associatedcamera devices 210. Processor 310 then identifies third camera feed set630 including, for example, camera feeds 8, 9, 10, 11, 12, and 13. Uponarriving at position F, processor 310 can determine that camera feed 12includes an optimal view of participant 110. In some embodiments, camerafeed 12 may be provided by a security camera, surveillance camera,wearable camera, an aircraft, blimp, or satellite. Server 220 candynamically switch the camera feed provided to conference bridge 252from camera feed 10 to camera feed 12. In some embodiments, processor310 can select camera feed 11 as an optimal feed, if camera feed 12contains only a very small, distant view of participant 110 (notillustrated in figures). Alternatively, if processor 310 determines thatneither camera feeds 11 nor camera feed 12 provides a sufficient view ofparticipant 110, processor 310 can decide whether to use participant110's own camera feed, or to discontinue providing a camera feed forparticipant 110 to the video conference.

As participant 110 approaches position G, processor 310 can predict thatparticipant 110 is walking toward position G and will soon be withinview in camera feed 13. For example, processor 310 can determine thatparticipant is traveling toward a dash camera installed in participant110's car and providing camera feed 13. Processor 310 can estimateparticipant 110's arrival based on participant 110's walking speed,direction, historical movement patterns, and distance from position G.Prior to reaching position G, server 220 may switch the camera feedprovided to conference bridge 252 from camera feed 12 to camera feed 13in transition 634. Once at position G, participant 110 might drive awayin his car while continuing the video conference with camera feed 13.Processor 310 can continue monitoring participant 110's area changes,but may continue using camera feed 13 because the associated dash camerais installed in participant 110's car and moving with participant 110.In some embodiments, processor 310 can continue determining participant110's area, identifying associated camera devices 210, and providingcamera feeds until the video conference ends or until participant 110and/or other participants 142 a and 142 b instruct server 220 todiscontinue the video portion of the conference (steps not shown infigure).

In some embodiments, processor 310 (and/or an external system associatedwith server 220) can determine an amount to compensate subscribers(owners and operators) for using their camera devices 210 for videoconferences (not shown in figures). In such embodiments, processor 310can calculate a length of time during which server 220 receives a camerafeed from the subscriber's camera device 210 for analysis, and/or alength of time during which server 220 provides the subscriber's cameradevice 210 camera feed to the video conference. In some embodiments,processor 310 can determine an amount to compensate subscribers formaking their camera devices 210 available for use by server 220, even ifprocessor 310 does not receive a camera feed from the camera device 210.Processor 310 can generate information regarding the determined amountof compensation, such as a monetary amount and a financial accountassociated with a subscriber for depositing the monetary amount. In someembodiments, compensation may be provided in the form of an accountcredit for a service associated with or partnered with server 220. Bycompensating subscribers (owners and operators) for leveraging theircamera devices 210, processor 310 has access to many camera devices 210to provide a continuous stream of clear images of participant 110,thereby increasing the quality of the video conference, and utilizingotherwise underused cameras throughout public and private environments.

Those skilled in the relevant arts would recognize that the dynamicnetworked camera selection methods and systems described herein could beused for purposes other than providing video feeds to conference calls.For example, the dynamic networked camera selection could be used inconjunction with security systems or surveillance systems, or forselecting cameras to provide camera feeds of an individual for abroadcast presentation of an event.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations of theembodiments will be apparent from consideration of the specification andpractice of the disclosed embodiments. For example, the describedimplementations include hardware and software, but systems and methodsconsistent with the present disclosure can be implemented as hardwarealone.

Computer programs based on the written description and methods of thisspecification are within the skill of a software developer. The variousprograms or program modules can be created using a variety ofprogramming techniques. For example, program sections or program modulescan be designed in or by means of Java, C, C++, assembly language, orany such programming languages. One or more of such software sections ormodules can be integrated into a computer system, non-transitorycomputer-readable media, or existing communications software.

Moreover, while illustrative embodiments have been described herein, thescope includes any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations or alterations based on the presentdisclosure. The elements in the claims are to be interpreted broadlybased on the language employed in the claims and not limited to examplesdescribed in the present specification or during the prosecution of theapplication, which examples are to be construed as non-exclusive.Further, the steps of the disclosed methods can be modified in anymanner, including by reordering steps or inserting or deleting steps. Itis intended, therefore, that the specification and examples beconsidered as non-limiting, with a true scope and spirit being indicatedby the following claims and their full scope of equivalents.

What is claimed is:
 1. A computer-implemented method for selecting acamera feed for a participant in a video conference, the methodcomprising: determining a first area of the participant; identifying,from a plurality of cameras associated with a plurality of subscribers,a first set of cameras associated with the first area; analyzing a firstplurality of real-time camera feeds from the first set of cameras; andautomatically selecting, by one or more processors and based on theanalysis, a first camera feed of the first plurality of real-time camerafeeds, the selected first camera feed containing a view of theparticipant.
 2. The method of claim 1, further comprising: providing thefirst camera feed to the video conference, wherein the plurality ofreal-time camera feeds include at least one of video or audio feeds. 3.The method of claim 2, further comprising: automatically selecting,based on the analysis, a second camera feed of the first plurality ofreal-time camera feeds if the second camera feed contains a better viewof the participant than the first camera feed; and automaticallyproviding the second camera feed to the video conference in place of thefirst camera feed.
 4. The method of claim 1, further comprising:receiving registration information for the plurality of camerasassociated with the plurality of subscribers, wherein the registrationinformation indicates a permission granted by the plurality ofsubscribers to access real-time camera feeds of the plurality of videocameras.
 5. The method of claim 1, wherein analyzing includes performinga facial recognition analysis to select an optimal view of theparticipant compared to the first plurality of real-time camera feeds.6. The method of claim 1, further comprising: determining a second areaof the participant different than the first area; identifying, from theplurality of cameras, a second set of cameras proximate the second area;analyzing a second plurality of real-time camera feeds from the secondset of cameras; selecting, based on the analysis, a second camera feedcontaining a view of the participant of the second plurality ofreal-time camera feeds; and automatically providing the second camerafeed to the video conference in place of the first camera feed.
 7. Themethod of claim 1, wherein the plurality of cameras include one or moreof a wearable camera, a fixed camera, a vehicle camera, or a mobiledevice camera.
 8. The method of claim 1, wherein the first area of theparticipant is determined using a locating mechanism associated with theparticipant.
 9. A system for selecting a camera feed for a participantin a video conference, the system comprising: one or more memorieshaving stored thereon computer-executable instructions; and one or moreprocessors configured to execute the stored instructions to: determine afirst area of the participant; identify, from a plurality of camerasassociated with a plurality of subscribers, a first set of camerasassociated with the first area; analyze a first plurality of real-timecamera feeds from the first set of cameras; and automatically select, bythe one or more processors and based on the analysis, a first camerafeed of the first plurality of real-time camera feeds, the selectedfirst camera feed containing a view of the participant.
 10. The systemof claim 9, wherein the one or more processors are further configured toexecute the stored instructions to: provide the first camera feed to thevideo conference, wherein the plurality of real-time camera feedsinclude at least one of video or audio feeds.
 11. The system of claim10, wherein the one or more processors are further configured to executethe stored instructions to: automatically select, based on the analysis,a second camera feed of the first plurality of real-time camera feeds ifthe second camera feed contains a better view of the participant thanthe first camera feed; and automatically provide the second camera feedto the video conference in place of the first camera feed.
 12. Thesystem of claim 9, wherein the one or more processors are furtherconfigured to execute the stored instructions to: receive registrationinformation for the plurality of cameras associated with the pluralityof subscribers, wherein the registration information indicates apermission granted by the plurality of subscribers to access real-timecamera feeds of the plurality of video cameras.
 13. The system of claim9, wherein the one or more processors are configured to analyze thefirst plurality of real-time camera feeds by performing a recognitionanalysis to select a camera feed having an optimal view of theparticipant compared to the first plurality of real-time camera feeds.14. The system of claim 9, wherein the one or more processors arefurther configured to execute the stored instructions to: determine asecond area of the participant different than the first area; identify,from the plurality of cameras, a second set of cameras proximate thesecond area; analyze a second plurality of real-time camera feeds fromthe second set of cameras; select, based on the analysis, a secondcamera feed containing a view of the participant of the second pluralityof real-time camera feeds; and automatically provide the second camerafeed to the video conference in place of the first camera feed.
 15. Anon-transitory computer-readable medium storing instructions that areexecutable by one or more processors to cause the one or more processorsto perform a method for selecting a camera feed for a participant in avideo conference, the method comprising: determining a first area of theparticipant; identifying, from a plurality of cameras associated with aplurality of subscribers, a first set of cameras associated with thefirst area; analyzing a first plurality of real-time camera feeds fromthe first set of cameras; and automatically selecting, by the one ormore processors and based on the analysis, a first camera feed of thefirst plurality of real-time camera feeds, the selected first camerafeed containing a view of the participant.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the set of instructionsthat are executable by the one or more processors to cause the one ormore processors to further perform: providing the first camera feed tothe video conference, wherein the plurality of real-time camera feedsinclude at least one of video or audio feeds.
 17. The non-transitorycomputer-readable medium of claim 16, wherein the set of instructionsthat are executable by the one or more processors to cause the one ormore processors to further perform: automatically selecting, based onthe analysis, a second camera feed of the first plurality of real-timecamera feeds if the second camera-feed contains a better view of theparticipant than the first camera feed; and automatically providing thesecond camera feed to the video conference in place of the first camerafeed.
 18. The non-transitory computer-readable medium of claim 15,wherein the set of instructions that are executable by the one or moreprocessors to cause the one or more processors to further perform:receiving registration information for the plurality of camerasassociated with the plurality of subscribers, wherein the registrationinformation indicates a permission granted by the plurality ofsubscribers to access real-time camera feeds of the plurality of videocameras.
 19. The non-transitory computer-readable medium of claim 15,wherein analyzing includes performing a recognition analysis to select acamera feed having an optimal view of the participant compared to thefirst plurality of real-time camera feeds.
 20. The non-transitorycomputer-readable medium of claim 15, wherein the set of instructionsthat are executable by the one or more processors to cause the one ormore processors to further perform: determining a second area of theparticipant different than the first area; identifying, from theplurality of cameras, a second set of cameras proximate to the secondarea; analyzing a second plurality of real-time camera feeds from thesecond set of cameras; selecting, based on the analysis, a second videocamera containing a view of the participant of the second plurality ofreal-time camera feeds; and automatically providing the second camerafeed to the video conference in place of the first camera feed.